


"""simple docstring"""






import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase							:    Any					   =	logging.get_logger(__name__)


_lowerCAmelCase							:    Union[str, Any]					   =	{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all BART models at https://huggingface.co/models?filter=bart
_lowerCAmelCase							:    Tuple					   =	{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
}

_lowerCAmelCase							:    List[str]					   =	{
    'facebook/bart-base': 1024,
    'facebook/bart-large': 1024,
    'facebook/bart-large-mnli': 1024,
    'facebook/bart-large-cnn': 1024,
    'facebook/bart-large-xsum': 1024,
    'yjernite/bart_eli5': 1024,
}







@lru_cache()
def        lowerCamelCase_(		)    ->     Optional[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				Optional[Any]							=     (
       list(range(ord("!"       )			,		ord("~"       ) + 1       )       ) + list(range(ord("¡"       )			,		ord("¬"       ) + 1       )       ) + list(range(ord("®"       )			,		ord("ÿ"       ) + 1       )       )
   )
   _lowerCamelCase   :				Union[str, Any]							=     bs[:]
   _lowerCamelCase   :				Optional[Any]							=     0
   for b in range(2**8       ):
      if b not in bs:
         bs.append(_lowerCamelCase       )
         cs.append(2**8 + n       )
         n += 1
   _lowerCamelCase   :				List[Any]							=     [chr(_lowerCamelCase       ) for n in cs]
   return dict(zip(_lowerCamelCase			,		_lowerCamelCase       )       )







def        lowerCamelCase_(		_lowerCamelCase       )    ->     Union[str, Any]:
   '''simple docstring'''





   _lowerCamelCase   :				Dict							=     set()
   _lowerCamelCase   :				Optional[int]							=     word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)       )
      _lowerCamelCase   :				Any							=     char
   return pairs




class 							A_      (       _a ):
  lowerCAmelCase__      			=	VOCAB_FILES_NAMES
  lowerCAmelCase__      			=	PRETRAINED_VOCAB_FILES_MAP
  lowerCAmelCase__      			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCAmelCase__      			=	['input_ids', 'attention_mask']


  def __init__(			self:						List[str]							,__lowerCAmelCase:						List[Any]							,__lowerCAmelCase:						Any							,__lowerCAmelCase:						int="replace"							,__lowerCAmelCase:						List[str]="<s>"							,__lowerCAmelCase:						Optional[int]="</s>"							,__lowerCAmelCase:						Any="</s>"							,__lowerCAmelCase:						Dict="<s>"							,__lowerCAmelCase:						Any="<unk>"							,__lowerCAmelCase:						int="<pad>"							,__lowerCAmelCase:						Optional[int]="<mask>"							,__lowerCAmelCase:						str=False							,**__lowerCAmelCase:						int							,):







     '''simple docstring'''







     _lowerCamelCase   :				Optional[int]							=     AddedToken(_A							,lstrip=_A							,rstrip=_A    ) if isinstance(_A							,_A    ) else bos_token
     _lowerCamelCase   :				List[str]							=     AddedToken(_A							,lstrip=_A							,rstrip=_A    ) if isinstance(_A							,_A    ) else eos_token
     _lowerCamelCase   :				Union[str, Any]							=     AddedToken(_A							,lstrip=_A							,rstrip=_A    ) if isinstance(_A							,_A    ) else sep_token
     _lowerCamelCase   :				Dict							=     AddedToken(_A							,lstrip=_A							,rstrip=_A    ) if isinstance(_A							,_A    ) else cls_token
     _lowerCamelCase   :				Tuple							=     AddedToken(_A							,lstrip=_A							,rstrip=_A    ) if isinstance(_A							,_A    ) else unk_token
     _lowerCamelCase   :				Union[str, Any]							=     AddedToken(_A							,lstrip=_A							,rstrip=_A    ) if isinstance(_A							,_A    ) else pad_token

     # Mask token behave like a normal word, i.e. include the space before it
     _lowerCamelCase   :				Optional[int]							=     AddedToken(_A							,lstrip=_A							,rstrip=_A    ) if isinstance(_A							,_A    ) else mask_token

     super().__init__(
         errors=_A							,bos_token=_A							,eos_token=_A							,unk_token=_A							,sep_token=_A							,cls_token=_A							,pad_token=_A							,mask_token=_A							,add_prefix_space=_A							,**_A							,)

     with open(_A							,encoding="utf-8"    ) as vocab_handle:
        _lowerCamelCase   :				Optional[Any]							=     json.load(_A    )
     _lowerCamelCase   :				Any							=     {v: k for k, v in self.encoder.items()}
     _lowerCamelCase   :				str							=     errors  # how to handle errors in decoding
     _lowerCamelCase   :				Any							=     bytes_to_unicode()
     _lowerCamelCase   :				int							=     {v: k for k, v in self.byte_encoder.items()}
     with open(_A							,encoding="utf-8"    ) as merges_handle:
        _lowerCamelCase   :				List[Any]							=     merges_handle.read().split("\n"    )[1:-1]
     _lowerCamelCase   :				List[Any]							=     [tuple(merge.split()    ) for merge in bpe_merges]
     _lowerCamelCase   :				List[Any]							=     dict(zip(_A							,range(len(_A    )    )    )    )
     _lowerCamelCase   :				str							=     {}
     _lowerCamelCase   :				Union[str, Any]							=     add_prefix_space

     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     _lowerCamelCase   :				Union[str, Any]							=     re.compile(r"\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"    )


  @property
  def 							_lowercase					(			self:						Dict    ):







     '''simple docstring'''







     return len(self.encoder    )


  def 							_lowercase					(			self:						Union[str, Any]    ):







     '''simple docstring'''







     return dict(self.encoder							,**self.added_tokens_encoder    )


  def 							_lowercase					(			self:						Tuple							,__lowerCAmelCase:						Union[str, Any]    ):







     '''simple docstring'''







     if token in self.cache:
        return self.cache[token]
     _lowerCamelCase   :				Union[str, Any]							=     tuple(_A    )
     _lowerCamelCase   :				List[Any]							=     get_pairs(_A    )

     if not pairs:
        return token

     while True:
        _lowerCamelCase   :				Optional[Any]							=     min(_A							,key=lambda __lowerCAmelCase    : self.bpe_ranks.get(_A							,float("inf"    )    )    )
        if bigram not in self.bpe_ranks:
           break
        _lowerCamelCase   :				Optional[Any]							=     bigram
        _lowerCamelCase   :				Optional[int]							=     []
        _lowerCamelCase   :				Optional[int]							=     0
        while i < len(_A    ):
           try:
              _lowerCamelCase   :				Tuple							=     word.index(_A							,_A    )
           except ValueError:
              new_word.extend(word[i:]    )
              break
           else:
              new_word.extend(word[i:j]    )
              _lowerCamelCase   :				Optional[int]							=     j

           if word[i] == first and i < len(_A    ) - 1 and word[i + 1] == second:
              new_word.append(first + second    )
              i += 2
           else:
              new_word.append(word[i]    )
              i += 1
        _lowerCamelCase   :				int							=     tuple(_A    )
        _lowerCamelCase   :				str							=     new_word
        if len(_A    ) == 1:
           break
        else:
           _lowerCamelCase   :				int							=     get_pairs(_A    )
     _lowerCamelCase   :				Union[str, Any]							=     ''' '''.join(_A    )
     _lowerCamelCase   :				Optional[int]							=     word
     return word


  def 							_lowercase					(			self:						Dict							,__lowerCAmelCase:						List[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Optional[int]							=     []
     for token in re.findall(self.pat							,_A    ):
        _lowerCamelCase   :				Union[str, Any]							=     ''''''.join(
            self.byte_encoder[b] for b in token.encode("utf-8"    )    )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
        bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_A    ).split(" "    )    )
     return bpe_tokens


  def 							_lowercase					(			self:						Dict							,__lowerCAmelCase:						Union[str, Any]    ):







     '''simple docstring'''







     return self.encoder.get(_A							,self.encoder.get(self.unk_token    )    )


  def 							_lowercase					(			self:						List[Any]							,__lowerCAmelCase:						Optional[Any]    ):







     '''simple docstring'''







     return self.decoder.get(_A    )


  def 							_lowercase					(			self:						int							,__lowerCAmelCase:						int    ):







     '''simple docstring'''







     _lowerCamelCase   :				Optional[int]							=     ''''''.join(_A    )
     _lowerCamelCase   :				Union[str, Any]							=     bytearray([self.byte_decoder[c] for c in text]    ).decode("utf-8"							,errors=self.errors    )
     return text


  def 							_lowercase					(			self:						Union[str, Any]							,__lowerCAmelCase:						str							,__lowerCAmelCase:						Optional[str] = None    ):







     '''simple docstring'''







     if not os.path.isdir(_A    ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
        return
     _lowerCamelCase   :				List[str]							=     os.path.join(
         _A							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )
     _lowerCamelCase   :				Tuple							=     os.path.join(
         _A							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]    )

     with open(_A							,"w"							,encoding="utf-8"    ) as f:
        f.write(json.dumps(self.encoder							,indent=2							,sort_keys=_A							,ensure_ascii=_A    ) + "\n"    )

     _lowerCamelCase   :				Any							=     0
     with open(_A							,"w"							,encoding="utf-8"    ) as writer:
        writer.write("#version: 0.2\n"    )
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,key=lambda __lowerCAmelCase    : kv[1]    ):
           if index != token_index:
              logger.warning(
                  F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                  " Please check that the tokenizer is not corrupted!"    )
              _lowerCamelCase   :				Tuple							=     token_index
           writer.write(" ".join(_A    ) + "\n"    )
           index += 1

     return vocab_file, merge_file


  def 							_lowercase					(			self:						int							,__lowerCAmelCase:						List[int]							,__lowerCAmelCase:						Optional[List[int]] = None    ):







     '''simple docstring'''







     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     _lowerCamelCase   :				str							=     [self.cls_token_id]
     _lowerCamelCase   :				List[Any]							=     [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep


  def 							_lowercase					(			self:						Tuple							,__lowerCAmelCase:						List[int]							,__lowerCAmelCase:						Optional[List[int]] = None							,__lowerCAmelCase:						bool = False    ):







     '''simple docstring'''







     if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=_A							,token_ids_a=_A							,already_has_special_tokens=_A    )

     if token_ids_a is None:
        return [1] + ([0] * len(_A    )) + [1]
     return [1] + ([0] * len(_A    )) + [1, 1] + ([0] * len(_A    )) + [1]


  def 							_lowercase					(			self:						Tuple							,__lowerCAmelCase:						List[int]							,__lowerCAmelCase:						Optional[List[int]] = None    ):







     '''simple docstring'''







     _lowerCamelCase   :				Optional[int]							=     [self.sep_token_id]
     _lowerCamelCase   :				int							=     [self.cls_token_id]

     if token_ids_a is None:
        return len(cls + token_ids_a + sep    ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]






  def 							_lowercase					(			self:						List[str]							,__lowerCAmelCase:						Dict							,__lowerCAmelCase:						int=False							,**__lowerCAmelCase:						int    ):







     '''simple docstring'''







     _lowerCamelCase   :				Union[str, Any]							=     kwargs.pop("add_prefix_space"							,self.add_prefix_space    )
     if (is_split_into_words or add_prefix_space) and (len(_A    ) > 0 and not text[0].isspace()):
        _lowerCamelCase   :				int							=     ''' ''' + text
     return (text, kwargs)







'''simple docstring'''





import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_UpperCamelCase						:						str  =					{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class  snake_case__      (					nn.Module):




					def __init__(  self  :		Optional[int]				, _A  :		Tuple      )    ->							List[str]:
								super().__init__()
								UpperCAmelCase_      :       Tuple          =       torchvision.models.resnetaaa(pretrained=_A      )
								UpperCAmelCase_      :       Union[str, Any]          =       list(model.children()      )[:-2]
								UpperCAmelCase_      :       Union[str, Any]          =       nn.Sequential(*_A      )
								UpperCAmelCase_      :       List[Any]          =       nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]      )





					def 		A      (  self  :		str				, _A  :		Optional[int]      )    ->							str:
								# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
								UpperCAmelCase_      :       List[str]          =       self.pool(self.model(_A      )      )
								UpperCAmelCase_      :       Tuple          =       torch.flatten(_A				, start_dim=2      )
								UpperCAmelCase_      :       Union[str, Any]          =       out.transpose(1				, 2      ).contiguous()
								return out  # BxNx2048






class  snake_case__      (					UpperCamelCase):




					def __init__(  self  :		Optional[int]				, _A  :		int				, _A  :		str				, _A  :		int				, _A  :		Dict				, _A  :		int      )    ->							List[str]:
								UpperCAmelCase_      :       Any          =       [json.loads(_A      ) for l in open(_A      )]
								UpperCAmelCase_      :       Tuple          =       os.path.dirname(_A      )
								UpperCAmelCase_      :       Any          =       tokenizer
								UpperCAmelCase_      :       Optional[Any]          =       labels
								UpperCAmelCase_      :       List[str]          =       len(_A      )
								UpperCAmelCase_      :       int          =       max_seq_length

								UpperCAmelCase_      :       str          =       transforms




					def __len__(  self  :		Tuple      )    ->							Tuple:
								return len(self.data      )




					def __getitem__(  self  :		Union[str, Any]				, _A  :		Tuple      )    ->							List[Any]:
								UpperCAmelCase_      :       Tuple          =       torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']				, add_special_tokens=_A      )      )
								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       sentence[0], sentence[1:-1], sentence[-1]
								UpperCAmelCase_      :       List[str]          =       sentence[: self.max_seq_length]

								UpperCAmelCase_      :       List[str]          =       torch.zeros(self.n_classes      )
								UpperCAmelCase_      :       Optional[int]          =       1

								UpperCAmelCase_      :       Tuple          =       Image.open(os.path.join(self.data_dir				, self.data[index]['''img''']      )      ).convert('''RGB'''      )
								UpperCAmelCase_      :       List[Any]          =       self.transforms(_A      )

								return {
								    "image_start_token": start_token,
								    "image_end_token": end_token,
								    "sentence": sentence,
								    "image": image,
								    "label": label,
								}





					def 		A      (  self  :		Tuple      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Tuple          =       Counter()
								for row in self.data:
											label_freqs.update(row['''label''']      )
								return label_freqs






def __UpperCAmelCase   (					A					:			Tuple		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       int          =       [len(row['''sentence''']		) for row in batch]
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       len(A		), max(A		)

			UpperCAmelCase_      :       Tuple          =       torch.zeros(A			,       A			,       dtype=torch.long		)
			UpperCAmelCase_      :       Dict          =       torch.zeros(A			,       A			,       dtype=torch.long		)

			for i_batch, (input_row, length) in enumerate(zip(A			,       A		)		):
						UpperCAmelCase_      :       int          =       input_row['''sentence''']
						UpperCAmelCase_      :       Optional[Any]          =       1

			UpperCAmelCase_      :       Union[str, Any]          =       torch.stack([row['''image'''] for row in batch]		)
			UpperCAmelCase_      :       Optional[int]          =       torch.stack([row['''label'''] for row in batch]		)
			UpperCAmelCase_      :       Any          =       torch.stack([row['''image_start_token'''] for row in batch]		)
			UpperCAmelCase_      :       int          =       torch.stack([row['''image_end_token'''] for row in batch]		)

			return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			return [
			    "Crime",
			    "Drama",
			    "Thriller",
			    "Action",
			    "Comedy",
			    "Romance",
			    "Documentary",
			    "Short",
			    "Mystery",
			    "History",
			    "Family",
			    "Adventure",
			    "Fantasy",
			    "Sci-Fi",
			    "Western",
			    "Horror",
			    "Sport",
			    "War",
			    "Music",
			    "Musical",
			    "Animation",
			    "Biography",
			    "Film-Noir",
			]







def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			return transforms.Compose(
			    [
			        transforms.Resize(2_5_6		),
			        transforms.CenterCrop(2_2_4		),
			        transforms.ToTensor(),
			        transforms.Normalize(
			            mean=[0.46777044, 0.44531429, 0.40661017]			,       std=[0.12221994, 0.12145835, 0.14380469]			,       ),
			    ]		)


